Text mining and data integration

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This document discusses text mining and data integration techniques used to extract information from biomedical literature and databases. It describes named entity recognition to identify concepts, co-mentioning analysis to find associations between entities, and using these methods along with experimental data and predictions to build integrated networks of genes and proteins and their relationships. These networks are made accessible through web resources that unify data from various sources under common identifiers and provide visualization and programmatic access.

Education Technology

Lars Juhl Jensen
Text mining and data integration

Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1
and this modification served as a priming
step to promote subsequent Cdc5-
dependent Swe1 hyperphosphorylation
and degradation

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
O’Donoghue et al., Journal of Web Semantics, 2010

Gene and protein names
Cue words for entity
recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome
genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]

Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

Korbel et al., Nature Biotechnology, 2004

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Kuhn et al., Nucleic Acids Research, 2012

This document discusses natural language processing and text mining techniques for biomedical literature and electronic health records. It describes named entity recognition to identify concepts like genes and proteins, relation extraction to find interactions between entities, and information extraction to formalize stated facts. It also discusses integrating extracted information with structured databases and visualizing relationships through web interfaces. Medical text mining can apply these techniques to clinical notes to identify diseases, drugs, adverse events and more for applications like comorbidity analysis, patient stratification, and pharmacovigilance.

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This document discusses various techniques in applied text mining, including named entity recognition, information extraction, and text/data integration. It covers extracting facts from text using natural language processing approaches like part-of-speech tagging and semantic tagging. It also discusses more pragmatic approaches using techniques like co-mentioning and guilt by association. The goal is to formalize biological facts and integrate text-derived information with databases of experimental data and computational predictions to build more comprehensive resources. Challenges include dealing with different data formats, identifiers, and quality across the many available databases.

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Lars Juhl Jensen

Text mining

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The document discusses text mining and natural language processing techniques for extracting information from scientific documents, including information retrieval, entity recognition, and information extraction. It provides examples of identifying entities like genes and proteins, and extracting relationships between entities, such as regulatory interactions, from text. The techniques discussed aim to formalize facts and biological knowledge from unstructured text into a structured database for further analysis.

The document discusses the human reference genome assembly GRCh38. It provides information on assembly basics, the assembly model used, updates made in GRCh38 compared to the previous version, and how to access and utilize the sequence and annotation data. Key points include that GRCh38 represents 178 genomic regions with alternative loci sequences, contains over 400kb of novel sequence from targeted updates, and is collaboratively maintained to integrate new data.

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This document discusses text mining approaches for extracting structured information from unstructured text sources like biomedical literature and electronic health records. It describes named entity recognition using dictionaries and rules to identify entities like genes and proteins. It also discusses extracting relationships between entities using cues like verbs. Precision and recall metrics are discussed for evaluating entity recognition. The document notes challenges like negations and proposes benchmarking approaches. It describes combining text mining with structured databases to unify biomedical information from various sources. Applications to electronic health records are discussed for tasks like comorbidity analysis.

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Diversity affects genome assembly in several ways. Complex diversity can lead to assembly errors and missing haplotype information when using old consensus models. It is important to represent diversity in genome assemblies to avoid missing sequences, haplotype configurations, gene models and overall knowledge. Improving genome assemblies with new reference models and techniques like linked-reads can help address these issues and improve individual genome analysis by better resolving allelic diversity, alternate loci and medically important genes.

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The document discusses updates to the human reference genome assembly GRCh38. It provides background on reference assemblies and describes how the Genome Reference Consortium manages and models genome assemblies. Key points include that GRCh38 contains refined centromere regions based on new data, novel sequence detections, and 261 alternate loci representing structural variants. The assembly is now incorporated into public sequence databases to improve access and use of the reference genome data.

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1. Discretization involves dividing the range of continuous attributes into intervals to reduce data size. Concept hierarchy formation recursively groups low-level concepts like numeric values into higher-level concepts like age groups. 2. Common techniques for discretization and concept hierarchy generation include binning, histogram analysis, clustering analysis, and entropy-based discretization. These techniques can be applied recursively to generate hierarchies. 3. Discretization and concept hierarchies reduce data size, provide more meaningful interpretations, and make data mining and analysis easier.

1.7 data reduction

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The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.

Data preprocessing

ankur bhalla

Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.

Data Mining: Data processing

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Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.

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Data Processing-Presentation

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DataPreProcessing

tdharmaputhiran

Data preprocessing involves transforming raw data into a clean and understandable format. It includes data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers and resolves inconsistencies. Data integration combines data from multiple sources. Data transformation performs operations like normalization and aggregation. Data reduction obtains a reduced representation of data to improve mining performance without losing essential information.

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Mining literature and medical records

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This document discusses mining literature and medical records using text mining techniques. It summarizes that text mining can be used to extract relevant information from large collections of scientific papers and medical records by using techniques like named entity recognition to identify concepts, information extraction to formalize stated facts, and analyzing co-mentioning of entities to find relationships. Challenges include the unstructured nature of medical records, differences between languages and formats, and privacy concerns when using patient health information. When applied carefully, text mining of literature and medical records can help identify new relationships and insights not captured in existing curated databases or help with medical research questions.

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A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.

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🔥🔥🔥🔥🔥🔥🔥🔥🔥 ‏ ‏إضغ بين إيديكم ‏من أقوى الملازم التي صممتها ملزمة تشريح الجهاز الهيكلي (نظري 3) 💀💀💀💀💀💀💀💀💀💀 تتميز هذهِ الملزمة بعِدة مُميزات : 1- مُترجمة ترجمة تُناسب جميع المستويات 2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!) #فهم_ماكو_درخ 3- دقة الكتابة والصور عالية جداً جداً جداً 4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً 5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني 6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊 🔥🔥🔥🔥🔥🔥🔥🔥🔥

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The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.

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Text mining and data integration

1. Lars Juhl Jensen Text mining and data integration

2. exponential growth

5. ~45 seconds per paper

6. information retrieval

7. named entity recognition

8. information extraction

9. association networks

10. data integration

11. information retrieval

12. find the relevant papers

13. ad hoc retrieval

14. user-specified query

15. “yeast AND cell cycle”

16. PubMed

17.

18. indexing

19. fast lookup

20. stemming

21. word endings

22. dynamic query expansion

23. MeSH terms

24. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

25. no tool will find that

26. named entity recognition

27. computer

28. as smart as a dog

29. teach it specific tricks

30.

31.

32. identify the concepts

33. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

34. comprehensive lexicon

35. CDC2

36. cyclin dependent kinase 1

37. orthographic variation

38. upper- and lower-case

39. CDC2

40. Cdc2

41. spaces and hyphens

42. cyclin dependent kinase 1

43. cyclin-dependent kinase 1

44. prefixes and postfixes

45. CDC2

46. hCDC2

47. “black list”

48. SDS

49. scalable implementation

50. >10 km <10 hours

51. augmented browsing

52. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

53. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

54. Reflect

55. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010

56. information extraction

57. formalize the facts

58. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

59. two approaches

60. co-mentioning

61. counting

62. within documents

63. within paragraphs

64. within sentences

65. co-mentioning score

66. NLP Natural Language Processing

67. grammatical analysis

68. part-of-speech tagging

69. multiword detection

70. semantic tagging

71. sentence parsing

72. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]

73. extract stated facts

74. high precision

75. poor recall

76. text corpus

77. most use abstracts

78. few use full-text articles

79. no access

80. PDF files

81.

82. layout-aware extraction

83. my corpus

84. ~22 million abstracts